ML Engineer
4 days ago
About Awign Expert:
Awign Expert, a division of Awign - India's largest work-as-a-service platform. We connect skilled professionals with exciting project-based opportunities from top companies, handling onboarding, feedback, conflict resolution, and payroll. Our mission is to empower professionals to focus on their work by managing administrative tasks, providing access to a network of renowned companies and rewarding assignments.Job
Experience: 6+ years
Joining: Immediate
Location: LRDE Layout, Doddanekundi, Doddanekkundi, Bengaluru, Karnataka 560037
F2F interview
Experience: 6+ years in AI and machine learning, with hands-on expertise in LLMs and agentic system development and deployment.
Requirement:● Proficiency in NLP & LLMs: Strong experience with large language models (GPT-3/4, Claude etc.) and understanding of transformer architectures, pretraining/fine-tuning, prompt engineering, and task adaptation.
● Agentic Systems Expertise: Knowledge in creating and deploying agentic systems with a goal-oriented design, leveraging reinforcement learning (RL) or other methods for autonomous task management.
● Technical Skills: Advanced proficiency in Python, ML libraries (Hugging Face Transformers, TensorFlow, PyTorch), and data processing libraries (Pandas, NumPy).
● MLOps & Deployment: Experience with MLOps practices, particularly for deploying large models and agentic systems in production environments (Docker, Kubernetes, MLFlow, or similar).
● Data Management: Familiarity with data engineering principles, including handling large datasets and real-time processing for model training, and experience with cloud platforms (AWS, GCP , Azure).
● Independent Problem Solving: Strong analytical skills with a proven track record of working independently to solve complex AI challenges.
● Communication Skills: Ability to clearly articulate technical concepts and AI-driven insights to non-technical stakeholders and document work for continuity.
Requirements
NLP, LLM (large language models), Agentic Systems, MLOps, Docker, Kubernetes, MLFlow, Python
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